Adaptive Gyroscope Bias Compensation for Vehicle Navigation
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Solution Overview
Problem
Low-cost MEMS gyroscopes in vehicle navigation systems lack precise self-calibration mechanisms and unit-specific characterization data, leading to navigation errors due to variations in readings under identical conditions, especially in environments where GNSS signals are degraded or unavailable.
Innovation Solution
A method for adaptive gyroscope bias compensation using a navigation filter to estimate and record gyroscope bias and temperature data, building a table of bias versus temperature and computing the rate of change of bias, allowing for linear extrapolation to estimate bias during temperature changes without a reference rotation rate, thereby improving navigation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If low-cost MEMS gyroscopes are used in vehicle navigation systems, then cost is reduced, but measurement precision deteriorates due to variations in readings under identical operating conditions
Solution Approach 1:
The patent applies parameter changes by modeling gyroscope bias as a function of temperature. Instead of attempting to manufacture more precise gyroscopes, the system changes the approach to compensate for variations by using temperature as a compensating parameter. The bias model uses temperature measurements to adjust and correct gyroscope readings, thereby maintaining measurement precision while continuing to use low-cost MEMS gyroscopes.
2Device complexity
If gyroscope bias compensation is implemented without unit-specific characterization data, then device complexity is reduced, but measurement precision deteriorates due to lack of precise self-calibration
Solution Approach 1:
The patent implements self-service by enabling the navigation system to automatically calibrate itself using readily available data. The system uses existing sensor measurements (gyroscope readings and temperature data) to build and update bias models without requiring external calibration equipment or manual intervention. This self-calibration approach maintains measurement precision while avoiding the complexity of external characterization devices or procedures.
Solution Approach 2:
The patent applies feedback by continuously monitoring gyroscope readings and temperature data, then using this information to update the bias model. The system compares expected behavior with actual measurements and adjusts the bias compensation accordingly. This closed-loop feedback mechanism ensures ongoing measurement precision without requiring complex pre-calibration procedures.
3Duration of action of stationary object
If dead reckoning is used to fill GNSS coverage gaps, then navigation continuity is improved, but measurement precision deteriorates due to accumulation of errors from uncorrected gyroscope variations
Solution Approach 1:
The patent applies preliminary action by pre-building gyroscope bias models during periods when reference data is available (such as when GNSS signals are present). These pre-established models are then applied during dead reckoning periods to correct gyroscope readings before errors can accumulate. This preliminary calibration action enables accurate navigation continuity through GNSS-denied environments by preventing error accumulation from uncorrected gyroscope variations.
Data Source
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AI summary
Adaptive gyroscope bias compensation allows a vehicle navigation module to estimate position and velocity reliably during temperature changes, wherein the vehicle navigation module comprises a MEMS gyroscope; and, a navigation filter that: (a) stores average gyroscope bias versus temperature data; (b) computes rate of change of gyroscope bias versus temperature from the average gyroscope bias versus temperature data; (c) estimates gyroscope bias by comparing rotation rate as measured by the gyroscope to a reference rotation rate, when a reference rotation rate is available; and, (d) estimates gyroscope bias, when no reference rotation rate is available, via linear extrapolation from a known bias value, the linear extrapolation taking into account a temperature change from the known bias value and rate of change of gyroscope bias versus temperature obtained in step (b).